In medical imaging, computer-aided detection (CAD) can supplement radiology reading tasks by providing specialized tools to assist in detecting or diagnosing lesions that may otherwise be difficult to evaluate. CAD systems can offer high sensitivity and increasing specificity. Breast and prostate MRI exams routinely generate hundreds or thousands of images that radiologists must exhaustively search for patterns of malignancy. CAD can alleviate this time-consuming task and offer additional computational analysis of potential malignancies. While CAD can reduce the time required to interpret images and prepare reports, it can still produce unwieldy amounts of information for radiologists to assess. In addition, it can produce results with lower specificity than a radiologist due to algorithmic constraints or access to a limited scope of information. These factors necessitate improvement in CAD methods and in evaluating CAD findings.
A CAD system can represent such findings as a collection of regions of interest (ROI), each ROI defined as a boundary in space representing the extent of the ROI. ROIs may also comprise quantified information about the morphology, composition, or suspected diagnosis of the underlying finding.
CAD findings are typically determined from one or more image series of a single imaging modality corresponding to an anatomical volume, although advances have been made in multi-modality CAD and CAD incorporating detailed patient information. In addition to performing CAD using images from multiple sources, it may be desirable to visualize CAD results using one or more reference images, or to obtain information from the reference images about regions described by the CAD results.
For some applications of CAD, such as dynamic contrast-enhanced MRI (DCE-MRI) of the breast and prostate, radiologists analyze kinetic curves of intensity over time to identify patterns that may indicate malignancy. These curves represent the presence of a contrast agent with the biological tissue over time and can be described by their wash-in, the rate of enhancement to a maximum, and wash-out, the rate at which they decrease from the maximum. Curve enhancement can be visualized using subtraction images. Both wash-in and wash-out can be visualized using parametric maps. While many abnormalities exhibit enhancement, malignancies are usually distinguished to one knowledgeable in the art by high wash-in and measurable wash-out rates. This distinction highlights the importance of access to a rich set of kinetic information when interpreting images.
In addition to kinetic characteristics, radiologists also assess the morphology of lesions. Although kinetic curves can assist in discriminating among lesion types, sometimes they are ambiguous and then morphology is used as a subsequent determining factor. 3D visualization of lesions can allow radiologists to quickly evaluate morphology.
Radiologists can use CAD systems for assistance in compiling reports, but they may also need assistance in sifting through voluminous CAD findings before compiling a report. A CAD system can offer a semi-automated workflow, where the system enhances certain patterns of data while relying on the user to select a seed location to drive further analysis. Other workflows offer a more automated approach, where the system itself takes the step of seeding and enumerating suspicious findings. Both workflows can produce extensive information, and an efficient approach is needed to evaluate this information. In addition, other medical specialties could benefit from visualizations that aid in the performance of their profession.
Surgeons can use visualization tools to orient, locate, and estimate resection margins and volume of lesions. Interactive visualization of lesions embedded within the target tissue could reduce the numbers of surgeries, reduce the time necessary for surgery, increase the accuracy of surgical resection, and aid in treatment judgments made at the time of or during surgery, such as lumpectomy or mastectomy.
Pathologists can use visualization tools to aid in localizing abnormalities within tissue specimens which have been resected by surgeons. They may section a specimen such as a mastectomy into varying slice thicknesses depending on certain general parameters prior to subjecting this tissue for mounting into slides or histopathological analysis. Pathologists' judgment in determining which slices of a gross specimen to process for immunohistochemical analysis is dependent on many factors. A flexible spatial visualization of suspected abnormalities within the specimen could guide pathologists and increase efficiency, regularize the pathological slicing procedure from case to case, reduce the necessary number of slices examined, reduce the number of slides that are processed from each slice, and reduce the number of false negative results.